Procedural Instance Timing for Real-Time Causal Environment Control
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Solution Overview
Problem
Existing techniques for controlling environments lack precision and efficiency in determining causal relationships between control settings and environment responses, particularly in dynamic systems, often requiring extensive computational resources and historical data, and are slow to adapt to changes.
Innovation Solution
A control system that identifies procedural instances within the environment, determines temporal extents, selects and monitors control settings, and adjusts internal parameters to attribute environment responses to a causal model, enabling real-time understanding and optimization of causal relationships while using fewer resources and less data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If modeling-based techniques are used to control the environment, then the system can learn from historical data, but the precision in determining causal relationships deteriorates
Solution Approach 1:
The patent segments the environment into procedural instances with specific temporal extents, allowing the system to analyze causal relationships within discrete, manageable time segments rather than treating all historical data as a single continuous stream. This segmentation enables more precise causal attribution by isolating specific cause-effect relationships in time.
Solution Approach 2:
The system dynamically adjusts the temporal extent parameters for different procedural instances based on the specific entities and contexts involved. Rather than using a fixed historical window, the temporal extent is optimized for each procedural instance, allowing the system to capture the appropriate causal timeframe for each situation while maintaining precision.
2Reliability
If active control techniques are used to generate knowledge, then the system can experiment with control settings, but the computational resources and time required increase
Solution Approach 1:
The system applies active control techniques selectively to specific procedural instances rather than continuously experimenting with all control settings. By applying control actions only where and when needed to answer specific causal questions, the system generates reliable knowledge while minimizing unnecessary computational overhead and time consumption.
Solution Approach 2:
The system uses feedback from environment responses to determine whether additional active control experimentation is needed. By monitoring whether sufficient causal knowledge has been obtained for each procedural instance, the system can stop active control experiments early when adequate knowledge is achieved, improving overall control efficiency while maintaining reliability.
3Adaptability or versatility
If the system adapts to changing environment relationships, then the control accuracy improves, but the time to respond to changes increases
Solution Approach 1:
The system segments environmental monitoring into discrete procedural instances with defined temporal extents. When changes are detected in one segment, the system can adapt its causal model for that specific segment without needing to reanalyze all historical data, enabling faster response to changes while maintaining adaptability.
Solution Approach 2:
The system pre-establishes causal models for multiple procedural instances with different temporal extents before changes occur. When environmental relationships change, the system can quickly select or switch to pre-computed models that are appropriate for the new conditions, reducing the time needed to adapt while maintaining accuracy.
4Measurement precision
If extensive historical data is used for control, then the model learning capability improves, but the computational resources required increase
Solution Approach 1:
The system extracts and utilizes only the specific historical data relevant to each procedural instance and its temporal extent, rather than processing all available historical data. By extracting only the necessary subset of historical information for each causal analysis, the system maintains model learning precision while significantly reducing computational resource consumption.
Solution Approach 2:
The system dynamically determines the appropriate historical data window for each procedural instance based on its temporal extent parameters. This dynamic adjustment allows the system to use more historical data when needed for complex causal relationships and less historical data for simpler cases, optimizing the balance between learning precision and computational resource usage.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining causal models for controlling environments. One of the methods includes identifying a procedural instance; determining a temporal extent for the procedural instance based on temporal extent parameters for the one or more entities in the procedural instance; selecting control settings for the procedural instance; monitoring environment responses to the control settings that are received for the one or more entities; determining which of the environment responses to attribute to the procedural instance in a causal model; and adjusting, based at least in part on the environment responses that are attributed to the procedural instance, the temporal extent parameters for the one or more entities.